SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7101–7125 of 10420 papers

TitleStatusHype
Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition—0
Achieving Explainability in a Visual Hard Attention Model through Content Prediction—0
Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty—0
Kernel Methods in Hyperbolic Spaces—0
Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks—0
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS—0
TaskSet: A Dataset of Optimization TasksCode0
Adaptive Dataset Sampling by Deep Policy Gradient—0
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method—0
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search SpaceCode0
Auto-view contrastive learning for few-shot image recognition—0
The simpler the better: vanilla sgd revisited—0
Unsupervised Domain Adaptation via Minimized Joint Error—0
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks—0
AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering—0
Graph Structural Aggregation for Explainable Learning—0
Counterfactual Thinking for Long-tailed Information Extraction—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
A Gradient-based Kernel Approach for Efficient Network Architecture Search—0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment—0
Context-Agnostic Learning Using Synthetic Data—0
Constructing Multiple High-Quality Deep Neural Networks: A TRUST-TECH Based Approach—0
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples—0
Toward Understanding Supervised Representation Learning with RKHS and GAN—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified